Ideas:

1 Transects

Plant Flowers Date lon lat ele Month Year julian
Glossoloma oblongicalyx 4 2015-10-19 -78.59093 0.130838 2270 October 2015 292
Gasteranthus quitensis 2 2016-10-17 -78.59770 0.120070 1940 October 2016 291
Kohleria affinis 1 2016-12-13 -78.59534 0.126746 2110 December 2016 348
Columnea ciliata 3 2014-02-27 -78.59934 0.116682 1960 February 2014 58
Columnea medicinalis 1 2014-04-23 -78.59372 0.128700 2130 April 2014 113
Drymonia teuscheri 3 2016-07-28 -78.59245 0.129393 2200 July 2016 210

2 Interactions

3 Phylogeny

4 Traits

Plant Flowers Date lon lat ele Month Year julian
Glossoloma oblongicalyx 4 2015-10-19 -78.59093 0.130838 2270 October 2015 292
Gasteranthus quitensis 2 2016-10-17 -78.59770 0.120070 1940 October 2016 291
Kohleria affinis 1 2016-12-13 -78.59534 0.126746 2110 December 2016 348
Columnea ciliata 3 2014-02-27 -78.59934 0.116682 1960 February 2014 58
Columnea medicinalis 1 2014-04-23 -78.59372 0.128700 2130 April 2014 113
Drymonia teuscheri 3 2016-07-28 -78.59245 0.129393 2200 July 2016 210

4.0.1 Total Flowers

4.1 Peak date

As range

4.2 Infer absences

4.3 Species elevation ranges

4.4 Flowering Data Matrix

5 Baseline model

Equal probability of flowering at anytime.

## sink("model/threshold_baseline.jags")
## cat("
##     model {
##     
##     for (x in 1:Nobs){
## 
##     #Observation of a flowering plant
##     Y[x] ~ dbern(p[x])
##     logit(p[x]) <- alpha[Plant[x]] 
##     
##     #Residuals
##     discrepancy[x] <- abs(Y[x] - p[x])
##     
##     #Assess Model Fit
##     Ynew[x] ~ dbern(p[x])
##     discrepancy.new[x]<-abs(Ynew[x] - p[x])
##     }
##     
##     
##     #Sum discrepancy
##     fit<-sum(discrepancy)/Nobs
##     fitnew<-sum(discrepancy.new)/Nobs
##     
##     #Prediction
##     
##     for(x in 1:Npreds){
##     #predict value
##     
##     #Observation - probability of flowering
##     prediction[x] ~ dbern(p_new[x])
##     logit(p_new[x])<-alpha[PredPlant[x]]
##     
##     #predictive error
##     pred_error[x] <- abs(Ypred[x] - prediction[x])
##     }
##     
##     
##     #Predictive Error
##     fitpred<-sum(pred_error)/Npreds
##     
##     #Priors
##     
##     #Species level priors
##     
##     for (j in 1:Plants){
##     
##     #Intercept
##     #Intercept flowering count
##     alpha[j] ~ dnorm(0,0.386)
##     
##     } 
## 
##     }
##     ",fill=TRUE)
## 
## sink()
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
## Graph information:
##    Observed stochastic nodes: 2324
##    Unobserved stochastic nodes: 2926
##    Total graph size: 14658
## 
## Initializing model

6 Get Chains

6.0.1 Evaluate convergence

6.0.2 Posterior estimates

7 Phylogeny

7.1 Attraction

## sink("model/threshold_attraction.jags")
## cat("
##     model {
##     
##     for (x in 1:Nobs){
##     
##     #Observation of a flowering plant
##     Y[x] ~ dbern(p[x])
##     logit(p[x]) <- e[Plant[x],Month[x]]
##     
##     #Residuals
##     discrepancy[x] <- abs(Y[x] - p[x])
##     
##     #Assess Model Fit
##     Ynew[x] ~ dbern(p[x])
##     discrepancy.new[x]<-abs(Ynew[x] - p[x])
##     }
##     
##     #Sum discrepancy
##     fit<-sum(discrepancy)/Nobs
##     fitnew<-sum(discrepancy.new)/Nobs
##     
##     #Prediction
##     
##     for(x in 1:Npreds){
##     #predict value
##     
##     #Observation - probability of flowering
##     prediction[x] ~ dbern(p_new[x])
##     logit(p_new[x])<- e[NewPlant[x],NewMonth[x]]
##     
##     #predictive error
##     pred_error[x] <- abs(Ypred[x] - prediction[x])
##     }
##     
##     #Predictive Error
##     fitpred<-sum(pred_error)/Npreds
##     
##     #########################
##     #autocorrelation in error
##     #########################
##     
##     #For each of observation
##     for(y in 1:Months){
##     e[1:Plants,y] ~ dmnorm(zeros,tauC[,])
##     }
##     
##     ##covariance among similiar species
##     for(i in 1:Plants){
##     for(j in 1:Plants){
##     C[i,j] = exp(-lambda_cov * D[i,j])
##     }
##     }
##     
##     ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
##     vCov = omega*C[,] + (1-omega) * I
##     tauC=inverse(vCov*gamma)
##     
##     #Priors
##     
##     #Autocorrelation priors
##     gamma  = 10
##     
##     #Strength of covariance decay
##     lambda_cov = 5
##     omega = 1
##     }
##     ",fill=TRUE)
## 
## sink()
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
## Graph information:
##    Observed stochastic nodes: 2324
##    Unobserved stochastic nodes: 2924
##    Total graph size: 18874
## 
## Initializing model

8 Get Chains

8.0.1 Evaluate convergence

8.0.2 Posterior estimates

Mean phylogenetic covariance

8.1 Decay in phylogenetic attraction

8.2 Repulsion

## sink("model/threshold_repulsion.jags")
## cat("
##     model {
##     
##     for (x in 1:Nobs){
##     
##     #Observation of a flowering plant
##     Y[x] ~ dbern(p[x])
##     logit(p[x]) <-  e[Plant[x],Month[x]]
##     
##     #Residuals
##     discrepancy[x] <- abs(Y[x] - p[x])
##     
##     #Assess Model Fit
##     Ynew[x] ~ dbern(p[x])
##     discrepancy.new[x]<-abs(Ynew[x] - p[x])
##     }
##     
##     
##     #Sum discrepancy
##     fit<-sum(discrepancy)/Nobs
##     fitnew<-sum(discrepancy.new)/Nobs
##     
##     #Prediction
##     
##     for(x in 1:Npreds){
##     #predict value
##     
##     #Observation - probability of flowering
##     prediction[x] ~ dbern(p_new[x])
##     logit(p_new[x])<- e[NewPlant[x],NewMonth[x]]
##     
##     #predictive error
##     pred_error[x] <- abs(Ypred[x] - prediction[x])
##     }
##     
##     #Predictive Error
##     fitpred<-sum(pred_error)/Npreds
##     
##     #########################
##     #autocorrelation in error
##     #########################
##     
##     #For each of observation
##     for(y in 1:Months){
##     e[1:Plants,y] ~ dmnorm(zeros,tauC[,])
##     }
##     
##     ##covariance among similiar species
##     for(i in 1:Plants){
##     for(j in 1:Plants){
##     C[i,j] = exp(-lambda_cov * D[i,j])
##     }
##     }
##     
##     ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
##     vCov = omega*C[,] + (1-omega) * I
##     tauC=vCov*gamma
##     
##     #Priors
##     
##     #Autocorrelation priors
##     gamma = 1
##     
##     #Strength of covariance decay
##     lambda_cov = 5
##     omega = 1
##     }
##     ",fill=TRUE)
## 
## sink()
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
## Graph information:
##    Observed stochastic nodes: 2324
##    Unobserved stochastic nodes: 2924
##    Total graph size: 18872
## 
## Initializing model

9 Get Chains

9.0.1 Evaluate convergence

9.0.2 Posterior estimates

Mean phylogenetic covariance martix

9.1 Decay in phylogenetic repulsion

10 Traits

10.1 Trait Attraction

## sink("model/threshold_attraction.jags")
## cat("
##     model {
##     
##     for (x in 1:Nobs){
##     
##     #Observation of a flowering plant
##     Y[x] ~ dbern(p[x])
##     logit(p[x]) <- e[Plant[x],Month[x]]
##     
##     #Residuals
##     discrepancy[x] <- abs(Y[x] - p[x])
##     
##     #Assess Model Fit
##     Ynew[x] ~ dbern(p[x])
##     discrepancy.new[x]<-abs(Ynew[x] - p[x])
##     }
##     
##     #Sum discrepancy
##     fit<-sum(discrepancy)/Nobs
##     fitnew<-sum(discrepancy.new)/Nobs
##     
##     #Prediction
##     
##     for(x in 1:Npreds){
##     #predict value
##     
##     #Observation - probability of flowering
##     prediction[x] ~ dbern(p_new[x])
##     logit(p_new[x])<- e[NewPlant[x],NewMonth[x]]
##     
##     #predictive error
##     pred_error[x] <- abs(Ypred[x] - prediction[x])
##     }
##     
##     #Predictive Error
##     fitpred<-sum(pred_error)/Npreds
##     
##     #########################
##     #autocorrelation in error
##     #########################
##     
##     #For each of observation
##     for(y in 1:Months){
##     e[1:Plants,y] ~ dmnorm(zeros,tauC[,])
##     }
##     
##     ##covariance among similiar species
##     for(i in 1:Plants){
##     for(j in 1:Plants){
##     C[i,j] = exp(-lambda_cov * D[i,j])
##     }
##     }
##     
##     ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
##     vCov = omega*C[,] + (1-omega) * I
##     tauC=inverse(vCov*gamma)
##     
##     #Priors
##     
##     #Autocorrelation priors
##     gamma  = 10
##     
##     #Strength of covariance decay
##     lambda_cov = 5
##     omega = 1
##     }
##     ",fill=TRUE)
## 
## sink()
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
## Graph information:
##    Observed stochastic nodes: 2324
##    Unobserved stochastic nodes: 2924
##    Total graph size: 19030
## 
## Initializing model

11 Get Chains

11.0.1 Evaluate convergence

11.0.2 Posterior estimates

11.1 Decay in trait attraction

11.2 Repulsion

## sink("model/threshold_repulsion.jags")
## cat("
##     model {
##     
##     for (x in 1:Nobs){
##     
##     #Observation of a flowering plant
##     Y[x] ~ dbern(p[x])
##     logit(p[x]) <-  e[Plant[x],Month[x]]
##     
##     #Residuals
##     discrepancy[x] <- abs(Y[x] - p[x])
##     
##     #Assess Model Fit
##     Ynew[x] ~ dbern(p[x])
##     discrepancy.new[x]<-abs(Ynew[x] - p[x])
##     }
##     
##     
##     #Sum discrepancy
##     fit<-sum(discrepancy)/Nobs
##     fitnew<-sum(discrepancy.new)/Nobs
##     
##     #Prediction
##     
##     for(x in 1:Npreds){
##     #predict value
##     
##     #Observation - probability of flowering
##     prediction[x] ~ dbern(p_new[x])
##     logit(p_new[x])<- e[NewPlant[x],NewMonth[x]]
##     
##     #predictive error
##     pred_error[x] <- abs(Ypred[x] - prediction[x])
##     }
##     
##     #Predictive Error
##     fitpred<-sum(pred_error)/Npreds
##     
##     #########################
##     #autocorrelation in error
##     #########################
##     
##     #For each of observation
##     for(y in 1:Months){
##     e[1:Plants,y] ~ dmnorm(zeros,tauC[,])
##     }
##     
##     ##covariance among similiar species
##     for(i in 1:Plants){
##     for(j in 1:Plants){
##     C[i,j] = exp(-lambda_cov * D[i,j])
##     }
##     }
##     
##     ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
##     vCov = omega*C[,] + (1-omega) * I
##     tauC=vCov*gamma
##     
##     #Priors
##     
##     #Autocorrelation priors
##     gamma = 1
##     
##     #Strength of covariance decay
##     lambda_cov = 5
##     omega = 1
##     }
##     ",fill=TRUE)
## 
## sink()
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
## Graph information:
##    Observed stochastic nodes: 2324
##    Unobserved stochastic nodes: 2924
##    Total graph size: 19028
## 
## Initializing model

12 Get Chains

12.0.1 Evaluate convergence

12.0.2 Posterior estimates

12.1 Decay in trait repulsion

13 Interaction

13.1 Attraction

## sink("model/threshold_attraction.jags")
## cat("
##     model {
##     
##     for (x in 1:Nobs){
##     
##     #Observation of a flowering plant
##     Y[x] ~ dbern(p[x])
##     logit(p[x]) <- e[Plant[x],Month[x]]
##     
##     #Residuals
##     discrepancy[x] <- abs(Y[x] - p[x])
##     
##     #Assess Model Fit
##     Ynew[x] ~ dbern(p[x])
##     discrepancy.new[x]<-abs(Ynew[x] - p[x])
##     }
##     
##     #Sum discrepancy
##     fit<-sum(discrepancy)/Nobs
##     fitnew<-sum(discrepancy.new)/Nobs
##     
##     #Prediction
##     
##     for(x in 1:Npreds){
##     #predict value
##     
##     #Observation - probability of flowering
##     prediction[x] ~ dbern(p_new[x])
##     logit(p_new[x])<- e[NewPlant[x],NewMonth[x]]
##     
##     #predictive error
##     pred_error[x] <- abs(Ypred[x] - prediction[x])
##     }
##     
##     #Predictive Error
##     fitpred<-sum(pred_error)/Npreds
##     
##     #########################
##     #autocorrelation in error
##     #########################
##     
##     #For each of observation
##     for(y in 1:Months){
##     e[1:Plants,y] ~ dmnorm(zeros,tauC[,])
##     }
##     
##     ##covariance among similiar species
##     for(i in 1:Plants){
##     for(j in 1:Plants){
##     C[i,j] = exp(-lambda_cov * D[i,j])
##     }
##     }
##     
##     ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
##     vCov = omega*C[,] + (1-omega) * I
##     tauC=inverse(vCov*gamma)
##     
##     #Priors
##     
##     #Autocorrelation priors
##     gamma  = 10
##     
##     #Strength of covariance decay
##     lambda_cov = 5
##     omega = 1
##     }
##     ",fill=TRUE)
## 
## sink()
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
## Graph information:
##    Observed stochastic nodes: 2324
##    Unobserved stochastic nodes: 2924
##    Total graph size: 19030
## 
## Initializing model

13.1.1 Evaluate convergence

13.1.2 Posterior estimates

Mean interaction covariance

13.2 Decay in interaction attraction

13.3 Repulsion

## sink("model/threshold_repulsion.jags")
## cat("
##     model {
##     
##     for (x in 1:Nobs){
##     
##     #Observation of a flowering plant
##     Y[x] ~ dbern(p[x])
##     logit(p[x]) <-  e[Plant[x],Month[x]]
##     
##     #Residuals
##     discrepancy[x] <- abs(Y[x] - p[x])
##     
##     #Assess Model Fit
##     Ynew[x] ~ dbern(p[x])
##     discrepancy.new[x]<-abs(Ynew[x] - p[x])
##     }
##     
##     
##     #Sum discrepancy
##     fit<-sum(discrepancy)/Nobs
##     fitnew<-sum(discrepancy.new)/Nobs
##     
##     #Prediction
##     
##     for(x in 1:Npreds){
##     #predict value
##     
##     #Observation - probability of flowering
##     prediction[x] ~ dbern(p_new[x])
##     logit(p_new[x])<- e[NewPlant[x],NewMonth[x]]
##     
##     #predictive error
##     pred_error[x] <- abs(Ypred[x] - prediction[x])
##     }
##     
##     #Predictive Error
##     fitpred<-sum(pred_error)/Npreds
##     
##     #########################
##     #autocorrelation in error
##     #########################
##     
##     #For each of observation
##     for(y in 1:Months){
##     e[1:Plants,y] ~ dmnorm(zeros,tauC[,])
##     }
##     
##     ##covariance among similiar species
##     for(i in 1:Plants){
##     for(j in 1:Plants){
##     C[i,j] = exp(-lambda_cov * D[i,j])
##     }
##     }
##     
##     ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
##     vCov = omega*C[,] + (1-omega) * I
##     tauC=vCov*gamma
##     
##     #Priors
##     
##     #Autocorrelation priors
##     gamma = 1
##     
##     #Strength of covariance decay
##     lambda_cov = 5
##     omega = 1
##     }
##     ",fill=TRUE)
## 
## sink()
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
## Graph information:
##    Observed stochastic nodes: 2324
##    Unobserved stochastic nodes: 2924
##    Total graph size: 19028
## 
## Initializing model

14 Get Chains

14.0.1 Evaluate convergence

14.0.2 Posterior estimates

Mean interaction covariance martix

14.1 Decay in interaction repulsion

15 Model Comparison

15.1 E: The effect of autocorrelation on mean flowering occurrence

15.1.1 E: The effect of autocorrelation

Dig into one example.

Glossoloma purpureum

16 By site

16.1 1300m - 1500m

16.2 1500-1700m

16.3 1700-1900

16.4 1900-2100m

16.5 2100-2300m

16.6 2300m - 2500m

16.7 Omega: The magnitude of the effect of autocorrelation on mean flowering occurrence

16.8 Gamma: The variance of the effect of autocorrelation on mean flowering occurrence

16.9 Lambda: The decay in autocorrelation effect

16.10 Decay in autocorrelation effect

17 Model Fit

17.1 Bayesian pvalue

## # A tibble: 7 x 2
##   Model                       p
##   <chr>                   <dbl>
## 1 baseline                0.598
## 2 interaction_attraction  0.151
## 3 interaction_repulsion   1    
## 4 phylogenetic_attraction 0.114
## 5 phylogenetic_repulsion  1    
## 6 trait_attraction        0.107
## 7 trait_repulsion         1

Without baseline

## # A tibble: 6 x 2
##   Model                       p
##   <chr>                   <dbl>
## 1 interaction_attraction  0.151
## 2 interaction_repulsion   1    
## 3 phylogenetic_attraction 0.114
## 4 phylogenetic_repulsion  1    
## 5 trait_attraction        0.107
## 6 trait_repulsion         1

17.2 Overall

Model mean lower upper
trait_repulsion 0.3571308 0.3493401 0.3644422
phylogenetic_repulsion 0.3433308 0.3354418 0.3510792
interaction_repulsion 0.3224660 0.3141719 0.3307715
interaction_attraction 0.2269077 0.2198348 0.2334874
phylogenetic_attraction 0.2256049 0.2187191 0.2324988
trait_attraction 0.2249972 0.2183616 0.2318602

17.2.1 Without baseline

17.3 By Species

Without baseline

Zoom in

18 Prediction

18.0.1 Tables

Model mean lower upper
trait_repulsion 0.3675204 0.3333333 0.4013605
phylogenetic_repulsion 0.3538197 0.3214286 0.3860544
interaction_repulsion 0.3315697 0.2993197 0.3656463
baseline 0.2475867 0.2210884 0.2738095
interaction_attraction 0.2301684 0.2057823 0.2534014
phylogenetic_attraction 0.2294507 0.2057823 0.2551020
trait_attraction 0.2284728 0.2039966 0.2534014

18.0.2 Flowering Rates among models

Plant Month Observed baseline interaction_attraction interaction_repulsion phylogenetic_attraction phylogenetic_repulsion trait_attraction trait_repulsion
Besleria solanoides 1 0.0 16.2 3.1 19.5 3.5 15.8 5.3 18.0
Besleria solanoides 2 0.0 17.0 2.5 16.8 2.3 14.2 3.8 19.4
Besleria solanoides 3 0.0 16.6 2.6 18.4 3.3 17.3 3.2 22.4
Besleria solanoides 4 13.3 16.8 13.0 27.2 14.7 23.4 13.3 30.1
Besleria solanoides 5 11.1 16.2 11.4 24.4 12.6 21.0 10.6 29.1
Besleria solanoides 6 10.0 16.9 10.1 22.1 11.2 18.6 9.4 27.2
Besleria solanoides 7 6.7 15.6 8.4 21.7 8.5 18.8 7.2 26.2
Besleria solanoides 8 8.3 16.5 9.4 25.2 11.0 22.3 11.3 28.9
Besleria solanoides 9 46.7 16.3 46.1 51.9 47.0 49.5 44.4 57.2
Besleria solanoides 10 57.1 16.7 56.8 61.2 57.3 55.7 55.1 64.1
Besleria solanoides 11 33.3 16.9 33.8 43.3 34.0 40.8 31.3 49.1
Besleria solanoides 12 12.5 16.0 13.6 32.6 15.1 27.5 12.8 35.1
Columnea ciliata 1 25.0 17.5 25.0 37.9 23.4 42.4 24.9 42.5
Columnea ciliata 2 43.8 17.8 42.6 53.0 42.2 56.4 42.0 54.5
Columnea ciliata 3 66.7 17.6 64.4 71.6 62.4 77.5 63.2 74.1
Columnea ciliata 4 46.7 17.7 45.7 55.5 44.7 61.4 45.9 58.6
Columnea ciliata 5 5.6 17.8 6.5 21.3 7.2 23.6 7.0 23.3
Columnea ciliata 6 10.0 18.0 10.3 23.1 11.0 25.1 10.4 24.6
Columnea ciliata 7 6.7 17.9 7.7 23.6 8.3 25.3 8.1 23.4
Columnea ciliata 8 0.0 18.4 3.1 21.4 3.1 24.7 3.1 22.1
Columnea ciliata 9 0.0 17.4 2.8 18.2 1.9 24.1 2.2 22.1
Columnea ciliata 10 0.0 18.4 2.5 19.6 1.8 24.8 2.2 22.2
Columnea ciliata 11 0.0 17.6 3.6 24.2 2.7 30.6 3.6 28.6
Columnea ciliata 12 0.0 17.7 3.5 27.7 2.8 33.4 3.0 30.6
Columnea kucyniakii 1 25.0 9.9 25.4 35.9 25.1 39.8 24.8 43.9
Columnea kucyniakii 2 43.8 9.6 42.7 48.7 43.2 54.2 41.7 57.7
Columnea kucyniakii 3 16.7 9.4 17.2 29.6 17.3 34.4 15.5 38.7
Columnea kucyniakii 4 6.7 9.5 7.7 20.3 7.9 26.2 8.3 26.3
Columnea kucyniakii 5 0.0 9.8 2.6 14.0 2.9 18.0 3.4 18.8
Columnea kucyniakii 6 0.0 9.2 2.3 13.6 2.5 16.7 3.2 18.5
Columnea kucyniakii 7 0.0 9.3 2.7 16.0 3.3 19.6 2.2 23.9
Columnea kucyniakii 8 0.0 9.2 3.5 18.1 3.0 22.5 2.9 25.4
Columnea kucyniakii 9 0.0 9.8 2.7 16.7 2.4 21.6 2.8 24.2
Columnea kucyniakii 10 0.0 9.6 2.7 16.5 1.8 22.3 1.6 26.8
Columnea kucyniakii 11 11.1 9.6 13.0 29.3 11.3 35.6 8.9 40.0
Columnea kucyniakii 12 12.5 9.1 14.2 30.5 13.5 38.8 11.8 39.8
Columnea medicinalis 1 16.7 18.3 17.6 32.3 17.9 35.4 16.9 33.6
Columnea medicinalis 2 12.5 18.4 12.8 27.6 12.8 30.0 13.1 27.3
Columnea medicinalis 3 25.0 19.0 23.9 38.4 23.6 44.9 23.5 40.8
Columnea medicinalis 4 6.7 18.3 7.3 23.1 7.5 28.4 6.7 25.4
Columnea medicinalis 5 11.1 18.4 11.5 26.1 12.0 27.2 11.8 26.0
Columnea medicinalis 6 30.0 18.4 29.3 40.3 29.3 41.4 30.9 39.2
Columnea medicinalis 7 33.3 18.4 32.3 45.7 32.3 49.0 32.1 44.6
Columnea medicinalis 8 25.0 17.9 23.6 40.4 23.7 44.9 25.4 40.6
Columnea medicinalis 9 6.7 18.2 7.8 24.8 6.8 29.6 6.1 27.0
Columnea medicinalis 10 28.6 18.4 26.9 42.5 25.6 49.7 25.8 45.1
Columnea medicinalis 11 11.1 18.5 11.2 32.8 9.1 39.4 9.8 36.9
Columnea medicinalis 12 0.0 18.0 3.1 27.8 3.6 32.4 2.7 28.8
Columnea picta 1 25.0 16.4 26.2 37.3 24.3 43.2 25.2 42.0
Columnea picta 2 12.5 16.0 13.2 27.9 12.9 29.6 13.4 26.9
Columnea picta 3 0.0 16.3 3.0 19.8 3.3 22.7 3.4 22.7
Columnea picta 4 13.3 16.9 13.7 29.2 13.6 32.7 13.2 32.0
Columnea picta 5 33.3 15.7 32.5 43.6 31.6 48.5 32.5 48.2
Columnea picta 6 35.0 16.2 34.6 44.4 34.2 46.3 32.5 48.2
Columnea picta 7 13.3 16.4 14.4 27.8 14.2 29.8 12.8 32.8
Columnea picta 8 8.3 16.2 8.9 27.2 9.4 30.4 7.9 30.4
Columnea picta 9 13.3 15.7 14.2 28.8 12.4 34.4 12.5 34.2
Columnea picta 10 0.0 16.9 2.6 19.2 2.9 23.6 1.8 24.1
Columnea picta 11 0.0 16.5 3.2 25.5 3.4 29.8 4.0 29.4
Columnea picta 12 12.5 15.8 12.7 34.2 9.4 41.8 13.5 38.5
Columnea strigosa 1 8.3 13.9 10.5 21.9 10.0 28.3 10.6 27.2
Columnea strigosa 2 12.5 13.2 13.8 22.0 13.7 28.8 13.8 26.4
Columnea strigosa 3 0.0 13.8 3.5 16.4 4.1 20.3 4.4 18.5
Columnea strigosa 4 0.0 13.2 2.7 13.7 3.3 20.6 2.5 19.2
Columnea strigosa 5 22.2 13.9 23.2 28.6 21.8 38.1 21.4 36.0
Columnea strigosa 6 40.0 13.4 40.5 42.1 38.5 51.2 40.2 47.5
Columnea strigosa 7 26.7 13.4 27.3 33.4 25.8 42.2 26.4 39.1
Columnea strigosa 8 16.7 13.5 18.4 28.2 15.8 37.6 17.1 33.6
Columnea strigosa 9 0.0 13.3 2.9 14.4 1.5 23.3 2.2 21.2
Columnea strigosa 10 0.0 13.5 2.9 14.9 1.9 24.6 3.0 19.9
Columnea strigosa 11 0.0 12.8 4.0 20.3 2.5 31.6 2.9 25.6
Columnea strigosa 12 0.0 12.6 4.5 20.9 3.0 32.0 2.3 28.9
Drymonia collegarum 1 16.7 14.8 17.3 31.4 17.0 35.1 17.2 35.5
Drymonia collegarum 2 6.2 14.1 7.4 21.8 7.1 26.5 6.6 28.1
Drymonia collegarum 3 8.3 14.5 9.5 24.5 10.7 28.3 11.6 30.2
Drymonia collegarum 4 13.3 14.4 14.6 26.8 15.2 32.4 14.3 32.7
Drymonia collegarum 5 22.2 14.3 21.7 33.4 22.2 34.5 23.6 36.0
Drymonia collegarum 6 15.0 14.5 15.2 25.9 15.4 29.2 15.3 31.9
Drymonia collegarum 7 13.3 14.2 14.2 27.4 14.0 32.1 14.1 33.8
Drymonia collegarum 8 16.7 15.2 17.5 31.7 17.2 36.4 17.2 36.8
Drymonia collegarum 9 13.3 14.4 14.5 28.5 11.9 35.2 13.2 34.7
Drymonia collegarum 10 7.1 14.3 9.1 22.9 8.6 30.2 8.8 28.2
Drymonia collegarum 11 11.1 14.0 11.0 31.1 10.4 38.1 11.4 37.4
Drymonia collegarum 12 25.0 13.9 23.5 43.2 17.7 52.8 19.7 50.2
Drymonia tenuis 1 16.7 15.1 17.8 30.1 16.7 34.9 18.4 34.4
Drymonia tenuis 2 18.8 14.6 19.1 29.8 18.8 36.4 18.0 37.9
Drymonia tenuis 3 25.0 14.7 25.7 36.8 25.2 41.7 22.3 45.8
Drymonia tenuis 4 13.3 14.9 14.6 26.4 13.9 30.6 14.2 33.8
Drymonia tenuis 5 22.2 14.8 22.4 31.4 22.2 35.2 20.4 39.8
Drymonia tenuis 6 20.0 15.1 20.7 29.4 19.9 33.6 19.1 37.7
Drymonia tenuis 7 20.0 15.3 20.2 30.0 20.6 36.1 16.6 41.8
Drymonia tenuis 8 8.3 15.3 9.4 24.8 9.2 29.0 10.1 31.2
Drymonia tenuis 9 6.7 14.5 7.7 21.0 7.2 26.2 8.5 26.9
Drymonia tenuis 10 7.1 14.7 8.0 22.5 8.3 27.9 10.1 25.7
Drymonia tenuis 11 0.0 13.8 2.9 23.2 3.9 27.8 7.9 22.9
Drymonia tenuis 12 0.0 14.3 4.1 24.5 3.9 29.6 6.8 27.7
Drymonia teuscheri 1 16.7 17.8 18.1 28.2 16.7 37.0 17.6 35.8
Drymonia teuscheri 2 6.2 18.1 7.7 19.1 7.3 26.4 7.0 27.3
Drymonia teuscheri 3 25.0 18.3 25.2 33.9 22.4 44.8 22.7 46.5
Drymonia teuscheri 4 26.7 18.0 26.9 35.7 24.9 43.5 25.6 44.3
Drymonia teuscheri 5 33.3 17.8 33.3 39.5 32.6 47.4 32.4 47.8
Drymonia teuscheri 6 20.0 17.9 21.1 28.5 19.6 35.3 19.5 37.1
Drymonia teuscheri 7 13.3 17.6 14.4 25.4 14.5 31.9 13.5 33.9
Drymonia teuscheri 8 25.0 18.3 25.9 35.2 22.7 44.4 23.4 43.4
Drymonia teuscheri 9 6.7 18.7 8.0 20.9 7.8 28.0 8.2 27.4
Drymonia teuscheri 10 14.3 17.9 14.9 26.2 12.9 37.3 13.6 34.6
Drymonia teuscheri 11 11.1 18.3 12.4 28.6 11.5 38.3 11.7 36.7
Drymonia teuscheri 12 0.0 18.1 4.4 22.6 6.7 28.9 6.2 27.7
Gasteranthus lateralis 1 33.3 15.4 33.1 41.6 33.6 39.0 31.8 50.2
Gasteranthus lateralis 2 12.5 15.6 13.4 25.4 13.3 23.4 13.1 33.2
Gasteranthus lateralis 3 8.3 15.5 9.9 24.0 9.7 24.3 9.8 31.0
Gasteranthus lateralis 4 13.3 15.5 14.1 25.2 14.3 25.2 13.4 32.7
Gasteranthus lateralis 5 5.6 15.8 5.7 19.0 6.7 17.9 7.5 23.4
Gasteranthus lateralis 6 5.0 15.4 5.9 17.5 6.2 16.2 7.5 23.6
Gasteranthus lateralis 7 0.0 15.6 2.7 16.5 2.1 15.9 4.3 22.4
Gasteranthus lateralis 8 16.7 15.9 17.4 30.3 17.2 27.8 16.9 38.2
Gasteranthus lateralis 9 13.3 15.3 13.6 26.7 13.9 24.3 13.1 32.4
Gasteranthus lateralis 10 28.6 15.5 29.0 38.0 29.9 34.2 26.4 44.6
Gasteranthus lateralis 11 44.4 15.8 42.3 52.0 44.1 49.8 37.3 64.1
Gasteranthus lateralis 12 25.0 15.0 24.2 40.3 26.0 38.5 21.2 51.1
Gasteranthus quitensis 1 41.7 12.5 40.6 47.5 41.1 46.3 38.3 54.6
Gasteranthus quitensis 2 18.8 12.6 19.7 30.6 19.6 27.8 17.0 33.7
Gasteranthus quitensis 3 8.3 11.7 10.1 23.8 9.5 23.1 8.7 28.6
Gasteranthus quitensis 4 6.7 13.3 7.9 20.4 8.4 19.9 8.6 23.8
Gasteranthus quitensis 5 0.0 12.6 2.4 15.4 2.2 14.7 2.6 17.5
Gasteranthus quitensis 6 0.0 12.5 2.2 13.9 2.1 12.7 2.8 17.3
Gasteranthus quitensis 7 6.7 12.2 7.6 21.0 7.9 20.6 7.4 24.9
Gasteranthus quitensis 8 16.7 12.7 17.8 29.9 17.3 28.2 16.8 32.5
Gasteranthus quitensis 9 13.3 12.4 14.1 27.0 14.3 24.0 15.7 24.4
Gasteranthus quitensis 10 21.4 12.2 22.4 32.4 22.4 30.3 23.0 29.4
Gasteranthus quitensis 11 11.1 12.3 12.1 29.1 14.1 25.7 15.0 27.9
Gasteranthus quitensis 12 12.5 13.0 14.5 31.4 15.5 28.0 13.7 32.9
Glossoloma oblongicalyx 1 0.0 15.2 3.4 19.6 4.1 23.2 3.6 23.3
Glossoloma oblongicalyx 2 0.0 15.4 2.6 17.6 2.2 19.9 3.2 19.4
Glossoloma oblongicalyx 3 0.0 15.5 3.2 19.2 2.5 23.4 3.3 22.4
Glossoloma oblongicalyx 4 0.0 15.4 2.6 16.6 2.5 22.2 3.5 19.7
Glossoloma oblongicalyx 5 11.1 15.5 11.4 23.4 10.7 28.0 11.1 29.9
Glossoloma oblongicalyx 6 10.0 15.6 10.7 21.6 10.5 25.2 10.2 28.0
Glossoloma oblongicalyx 7 33.3 15.4 32.7 42.3 33.5 46.2 32.5 50.4
Glossoloma oblongicalyx 8 33.3 15.2 33.5 43.4 33.3 48.9 30.8 52.3
Glossoloma oblongicalyx 9 33.3 15.5 33.0 41.8 32.6 47.4 32.1 50.9
Glossoloma oblongicalyx 10 35.7 15.5 34.5 43.2 34.3 50.5 32.0 54.3
Glossoloma oblongicalyx 11 22.2 15.3 22.6 38.0 21.0 43.4 18.7 49.0
Glossoloma oblongicalyx 12 0.0 14.8 4.3 24.8 3.8 28.3 3.8 32.0
Glossoloma purpureum 1 16.7 13.7 17.7 31.7 15.8 35.9 15.7 37.9
Glossoloma purpureum 2 12.5 13.2 12.9 25.6 12.3 30.0 11.2 30.8
Glossoloma purpureum 3 0.0 13.1 2.5 20.2 2.5 22.9 2.6 23.3
Glossoloma purpureum 4 6.7 12.9 7.6 22.5 6.7 27.1 7.1 26.9
Glossoloma purpureum 5 5.6 13.1 6.5 20.2 6.5 22.8 6.4 23.9
Glossoloma purpureum 6 10.0 13.0 10.5 23.2 10.3 25.0 10.0 26.9
Glossoloma purpureum 7 20.0 12.8 19.7 33.1 20.4 34.4 21.5 36.3
Glossoloma purpureum 8 16.7 12.9 16.7 31.9 17.7 33.5 18.1 35.2
Glossoloma purpureum 9 26.7 13.0 26.9 37.3 26.1 42.1 26.5 42.7
Glossoloma purpureum 10 14.3 12.8 14.5 28.7 15.1 32.0 14.9 34.3
Glossoloma purpureum 11 11.1 12.8 11.8 31.2 12.0 33.6 12.7 35.4
Glossoloma purpureum 12 12.5 13.1 14.2 34.1 12.6 39.4 11.0 39.3
Kohleria affinis 1 8.3 17.3 10.3 22.1 9.7 21.5 9.2 30.9
Kohleria affinis 2 25.0 17.1 25.3 33.0 25.7 31.6 24.7 39.7
Kohleria affinis 3 41.7 17.4 40.6 46.6 42.5 45.2 39.5 56.2
Kohleria affinis 4 46.7 17.6 45.6 50.8 47.0 48.0 45.5 60.2
Kohleria affinis 5 27.8 17.1 27.5 35.0 28.4 33.2 27.3 44.5
Kohleria affinis 6 15.0 17.0 15.6 24.1 15.7 22.7 14.4 31.8
Kohleria affinis 7 0.0 17.5 2.3 15.5 3.0 14.7 2.2 22.4
Kohleria affinis 8 8.3 17.1 10.2 22.3 10.7 21.5 8.2 32.7
Kohleria affinis 9 6.7 17.3 8.0 20.4 8.2 18.5 6.8 28.4
Kohleria affinis 10 0.0 16.9 3.3 16.2 2.6 14.5 2.2 24.9
Kohleria affinis 11 0.0 17.7 4.0 20.4 3.8 18.4 3.7 30.4
Kohleria affinis 12 12.5 17.2 13.5 28.2 14.8 27.4 11.8 41.0